回归树预测

本文不具体介绍回归树的具体算法，采用波士顿房价预测的案例来使用回归树模型。语言是Python3.6，集成环境是Anaconda3。

#导入数据from sklearn.datasets import load_bostonboston=load_boston()print(boston.DESCR)from sklearn.cross_validation import train_test_splitimport numpy as npX=boston.datay=boston.targetX_train,X_test,y_train,y_test=train_test_split(X,y,random_state=33,test_size=0.25)#分析回归目标值的差异print('max:',np.max(boston.target),'\tmin:',np.min(boston.target),'\taverage:',np.mean(boston.target))from sklearn.preprocessing import StandardScalerss_X=StandardScaler()ss_y=StandardScaler()#标准化处理X_train=ss_X.fit_transform(X_train)X_test=ss_X.fit_transform(X_test)y_train=ss_y.fit_transform(y_train)y_test=ss_y.fit_transform(y_test)#导入回归树from sklearn.tree import DecisionTreeRegressor#使用默认配置初始化DecisionTreeRegressordtr=DecisionTreeRegressor()dtr.fit(X_train,y_train)dtr_y_predict=dtr.predict(X_test)#性能测评print('R-squared value of DecisionTreeRegressor:',dtr.score(X_test,y_test))